AIF-C01 Fundamentals of Generative AI Practice Question
Which THREE are key capabilities of Amazon Bedrock? (Choose 3)
⚠ Common exam trap
A common misconception is that Amazon Bedrock includes a built-in vector database for knowledge bases, when in fact it integrates with external vector stores such as Amazon OpenSearch Serverless or Pinecone. Another misconception is that Bedrock automatically selects the best model for the use case, whereas users must manually evaluate and choose models based on performance metrics and specific requirements.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Model customization through fine-tuning
Amazon Bedrock provides model customization through fine-tuning (B), allowing you to adapt supported foundation models with your own labeled data to improve performance for domain-specific tasks. It also offers Guardrails for Amazon Bedrock (C), which let you define policies that filter harmful or inappropriate content and enforce topics and sensitive-information redaction across model responses. Bedrock additionally delivers serverless inference for foundation models (D), so you can invoke models via a managed API without provisioning or managing any underlying infrastructure. Option A is not a Bedrock capability because Bedrock does not automatically choose a model for you; the developer selects the model. Option E is incorrect because Bedrock itself does not include a built-in vector database; knowledge bases for Amazon Bedrock integrate with separate vector stores such as Amazon OpenSearch Serverless or Amazon Aurora.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Automatic model selection based on use case
Why it's wrong here
Bedrock exposes foundation models through a unified API but requires you to choose the model explicitly; it performs no automatic selection. It is tempting because Bedrock does offer model evaluation and comparison tooling, which would be the right choice when benchmarking candidate models for a use case rather than selecting one at runtime.
- ✓
Model customization through fine-tuning
Why this is correct
Fine-tuning adjusts a foundation model's weights using your labelled dataset, tailoring outputs to domain-specific tasks. This satisfies the stem's requirement for a key Bedrock capability, since Bedrock supports custom models trained on your data, alongside provisioned throughput for hosting them.
- ✓
Guardrails to filter harmful content
Why this is correct
Guardrails provide configurable content filtering, denying harmful inputs and outputs against defined policies, which satisfies the safety constraint in the stem. This is a native Bedrock capability, distinct from model training or hosting, letting teams enforce responsible-AI controls across supported foundation models without building custom moderation layers.
- ✓
Serverless inference for foundation models
Why this is correct
Serverless inference removes infrastructure provisioning, letting you invoke foundation models through a managed API without managing instances. This satisfies the stem's capability requirement by delivering on-demand, pay-per-use access to models from Amazon and third parties, scaling automatically with request volume rather than requiring capacity planning.
- ✗
Built-in vector database for knowledge bases
Why it's wrong here
Bedrock provides a Knowledge Bases feature but no built-in vector database; it connects to Amazon OpenSearch Serverless, Aurora or Neptune Analytics as the vector store. It is tempting because Knowledge Bases genuinely handle retrieval-augmented generation; that would be correct when the requirement is managed RAG, not a self-contained vector engine.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.